Cargando…

A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data

The financial risk not only affects the development of the company itself, but also affects the economic development of the whole society; therefore, the financial risk assessment of company is an important part. At present, numerous methods of financial risk assessment have been researched by schol...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Dan-Ping, Cheng, Si-Jie, Cheng, Peng-Fei, Wang, Jian-Qiang, Zhang, Hong-Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6306178/
https://www.ncbi.nlm.nih.gov/pubmed/30586437
http://dx.doi.org/10.1371/journal.pone.0208166
_version_ 1783382726069452800
author Li, Dan-Ping
Cheng, Si-Jie
Cheng, Peng-Fei
Wang, Jian-Qiang
Zhang, Hong-Yu
author_facet Li, Dan-Ping
Cheng, Si-Jie
Cheng, Peng-Fei
Wang, Jian-Qiang
Zhang, Hong-Yu
author_sort Li, Dan-Ping
collection PubMed
description The financial risk not only affects the development of the company itself, but also affects the economic development of the whole society; therefore, the financial risk assessment of company is an important part. At present, numerous methods of financial risk assessment have been researched by scholars. However, most of the extant methods neither integrated fuzzy sets with quantitative analysis, nor took into account the historical data of the past few years. To settle these defects, this paper proposes a novel financial risk assessment model for companies based on heterogeneous multiple-criteria decision-making (MCDM) and historical data. Subjective and objective indexes are comprehensively taken into consideration in the financial risk assessment index system of the model, which combines fuzzy theory with quantitative data analysis. Moreover, the assessment information obtained from historical financial information of company, credit rating agency and decision makers, including crisp numbers, triangular fuzzy numbers and neutrosophic numbers. Furthermore, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to determine the ranking order of companies according to their financial risk. Finally, an empirical study of financial risk assessment for companies is conducted, and the results of comparative analysis and sensitivity analysis suggest that the proposed model can effectively and reliably obtain the company with the lowest financial risk.
format Online
Article
Text
id pubmed-6306178
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-63061782019-01-08 A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data Li, Dan-Ping Cheng, Si-Jie Cheng, Peng-Fei Wang, Jian-Qiang Zhang, Hong-Yu PLoS One Research Article The financial risk not only affects the development of the company itself, but also affects the economic development of the whole society; therefore, the financial risk assessment of company is an important part. At present, numerous methods of financial risk assessment have been researched by scholars. However, most of the extant methods neither integrated fuzzy sets with quantitative analysis, nor took into account the historical data of the past few years. To settle these defects, this paper proposes a novel financial risk assessment model for companies based on heterogeneous multiple-criteria decision-making (MCDM) and historical data. Subjective and objective indexes are comprehensively taken into consideration in the financial risk assessment index system of the model, which combines fuzzy theory with quantitative data analysis. Moreover, the assessment information obtained from historical financial information of company, credit rating agency and decision makers, including crisp numbers, triangular fuzzy numbers and neutrosophic numbers. Furthermore, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to determine the ranking order of companies according to their financial risk. Finally, an empirical study of financial risk assessment for companies is conducted, and the results of comparative analysis and sensitivity analysis suggest that the proposed model can effectively and reliably obtain the company with the lowest financial risk. Public Library of Science 2018-12-26 /pmc/articles/PMC6306178/ /pubmed/30586437 http://dx.doi.org/10.1371/journal.pone.0208166 Text en © 2018 Li et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Li, Dan-Ping
Cheng, Si-Jie
Cheng, Peng-Fei
Wang, Jian-Qiang
Zhang, Hong-Yu
A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data
title A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data
title_full A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data
title_fullStr A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data
title_full_unstemmed A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data
title_short A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data
title_sort novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6306178/
https://www.ncbi.nlm.nih.gov/pubmed/30586437
http://dx.doi.org/10.1371/journal.pone.0208166
work_keys_str_mv AT lidanping anovelfinancialriskassessmentmodelforcompaniesbasedonheterogeneousinformationandaggregatedhistoricaldata
AT chengsijie anovelfinancialriskassessmentmodelforcompaniesbasedonheterogeneousinformationandaggregatedhistoricaldata
AT chengpengfei anovelfinancialriskassessmentmodelforcompaniesbasedonheterogeneousinformationandaggregatedhistoricaldata
AT wangjianqiang anovelfinancialriskassessmentmodelforcompaniesbasedonheterogeneousinformationandaggregatedhistoricaldata
AT zhanghongyu anovelfinancialriskassessmentmodelforcompaniesbasedonheterogeneousinformationandaggregatedhistoricaldata
AT lidanping novelfinancialriskassessmentmodelforcompaniesbasedonheterogeneousinformationandaggregatedhistoricaldata
AT chengsijie novelfinancialriskassessmentmodelforcompaniesbasedonheterogeneousinformationandaggregatedhistoricaldata
AT chengpengfei novelfinancialriskassessmentmodelforcompaniesbasedonheterogeneousinformationandaggregatedhistoricaldata
AT wangjianqiang novelfinancialriskassessmentmodelforcompaniesbasedonheterogeneousinformationandaggregatedhistoricaldata
AT zhanghongyu novelfinancialriskassessmentmodelforcompaniesbasedonheterogeneousinformationandaggregatedhistoricaldata